As shown in Fig. REF , the proposed AFC module is utilized to encode the input. Recently, some studies suggest that the long-range feature dependencies can be captured by the self-attention mechanism. Hu et al. [1]} demonstrated it in large scale image recognition task. In this paper, we introduce the self-attention mechanism into the SAR image change detection task, and designed a simple yet effective AFC module. First, a set of convolutions (Conv 1-1, Conv 1-2 and Conv 1-3 with kernel size \(3\times 3\) ) are employed with different dilation rates, which are set to 1, 2 and 3 to capture multiscale features. Then, the multiscale features are aggregated by feature fusion based on channel-wise attention (CA).
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